import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		"▁"

_lowerCAmelCase       =		{"vocab_file": "sentencepiece.bpe.model"}

_lowerCAmelCase       =		{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

_lowerCAmelCase       =		{
    "facebook/nllb-200-distilled-600M": 1_024,
}

# fmt: off
_lowerCAmelCase       =		["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     VOCAB_FILES_NAMES
							UpperCAmelCase				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase				   =     PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase				   =     ["input_ids", "attention_mask"]

							UpperCAmelCase				   =     []
							UpperCAmelCase				   =     []







							def __init__(   self      :     int ,			_A      :     Union[str, Any] ,			_A      :     Optional[int]="<s>" ,			_A      :     str="</s>" ,			_A      :     Optional[int]="</s>" ,			_A      :     Tuple="<s>" ,			_A      :     Dict="<unk>" ,			_A      :     List[str]="<pad>" ,			_A      :     Dict="<mask>" ,			_A      :     Optional[Any]=None ,			_A      :     Union[str, Any]=None ,			_A      :     Any=None ,			_A      :     Optional[Dict[str, Any]] = None ,			_A      :     List[str]=None ,			_A      :     str=False ,			**_A      :     int ,			):
												# Mask token behave like a normal word, i.e. include the space before it
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else mask_token

												_UpperCamelCase			     =     {} if sp_model_kwargs is None else sp_model_kwargs
												_UpperCamelCase			     =     legacy_behaviour

												super().__init__(
												    bos_token=_A ,			eos_token=_A ,			unk_token=_A ,			sep_token=_A ,			cls_token=_A ,			pad_token=_A ,			mask_token=_A ,			tokenizer_file=_A ,			src_lang=_A ,			tgt_lang=_A ,			additional_special_tokens=_A ,			sp_model_kwargs=self.sp_model_kwargs ,			legacy_behaviour=_A ,			**_A ,			)

												_UpperCamelCase			     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(str(_A		)		)
												_UpperCamelCase			     =     vocab_file
												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
												# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
												# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

												# Mimic fairseq token-to-id alignment for the first 4 token
												_UpperCamelCase			     =     {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
												_UpperCamelCase			     =     1

												_UpperCamelCase			     =     len(self.sp_model		)
												_UpperCamelCase			     =     {
												    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_A		)
												}
												_UpperCamelCase			     =     {v: k for k, v in self.lang_code_to_id.items()}
												_UpperCamelCase			     =     len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

												self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
												_UpperCamelCase			     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
												_UpperCamelCase			     =     list(self.lang_code_to_id.keys()		)

												if additional_special_tokens is not None:
																	# Only add those special tokens if they are not already there.
																	self._additional_special_tokens.extend(
																	    [t for t in additional_special_tokens if t not in self._additional_special_tokens]		)

												_UpperCamelCase			     =     src_lang if src_lang is not None else '''eng_Latn'''
												_UpperCamelCase			     =     self.lang_code_to_id[self._src_lang]
												_UpperCamelCase			     =     tgt_lang
												self.set_src_lang_special_tokens(self._src_lang		)







							def __getstate__(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     self.__dict__.copy()
												_UpperCamelCase			     =     None
												_UpperCamelCase			     =     self.sp_model.serialized_model_proto()
												return state







							def __setstate__(   self      :     str ,			_A      :     Any		):
												_UpperCamelCase			     =     d

												# for backward compatibility
												if not hasattr(self ,			'''sp_model_kwargs'''		):
																	_UpperCamelCase			     =     {}

												_UpperCamelCase			     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return self._src_lang







							@src_lang.setter
							def  UpperCamelCase_						(   self      :     int ,			_A      :     str		):
												_UpperCamelCase			     =     new_src_lang
												self.set_src_lang_special_tokens(self._src_lang		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None ,			_A      :     bool = False		):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_A ,			token_ids_a=_A ,			already_has_special_tokens=_A		)

												_UpperCamelCase			     =     [1] * len(self.prefix_tokens		)
												_UpperCamelCase			     =     [1] * len(self.suffix_tokens		)
												if token_ids_a is None:
																	return prefix_ones + ([0] * len(_A		)) + suffix_ones
												return prefix_ones + ([0] * len(_A		)) + ([0] * len(_A		)) + suffix_ones







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												_UpperCamelCase			     =     [self.sep_token_id]
												_UpperCamelCase			     =     [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep		) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     str ,			_A      :     str ,			_A      :     Optional[str] ,			_A      :     Optional[str] ,			**_A      :     Optional[int]		):
												if src_lang is None or tgt_lang is None:
																	raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
												_UpperCamelCase			     =     src_lang
												_UpperCamelCase			     =     self(_A ,			add_special_tokens=_A ,			return_tensors=_A ,			**_A		)
												_UpperCamelCase			     =     self.convert_tokens_to_ids(_A		)
												_UpperCamelCase			     =     tgt_lang_id
												return inputs







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     {self.convert_ids_to_tokens(_A		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab







							def  UpperCamelCase_						(   self      :     int ,			_A      :     str		):
												return self.sp_model.encode(_A ,			out_type=_A		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[int]		):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												_UpperCamelCase			     =     self.sp_model.PieceToId(_A		)

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     str		):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     Dict		):
												_UpperCamelCase			     =     ''''''.join(_A		).replace(_A ,			''' '''		).strip()
												return out_string







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     str ,			_A      :     Optional[str] = None		):
												if not os.path.isdir(_A		):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																	return
												_UpperCamelCase			     =     os.path.join(
												    _A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(_A		) and os.path.isfile(self.vocab_file		):
																	copyfile(self.vocab_file ,			_A		)
												elif not os.path.isfile(self.vocab_file		):
																	with open(_A ,			'''wb'''		) as fi:
																						_UpperCamelCase			     =     self.sp_model.serialized_model_proto()
																						fi.write(_A		)

												return (out_vocab_file,)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     List[str] ,			_A      :     str = "eng_Latn" ,			_A      :     Optional[List[str]] = None ,			_A      :     str = "fra_Latn" ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     src_lang
												_UpperCamelCase			     =     tgt_lang
												return super().prepare_seqaseq_batch(_A ,			_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return self.set_src_lang_special_tokens(self.src_lang		)







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												return self.set_tgt_lang_special_tokens(self.tgt_lang		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     Tuple		):
												_UpperCamelCase			     =     self.lang_code_to_id[src_lang]
												if self.legacy_behaviour:
																	_UpperCamelCase			     =     []
																	_UpperCamelCase			     =     [self.eos_token_id, self.cur_lang_code]
												else:
																	_UpperCamelCase			     =     [self.cur_lang_code]
																	_UpperCamelCase			     =     [self.eos_token_id]







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     str		):
												_UpperCamelCase			     =     self.lang_code_to_id[lang]
												if self.legacy_behaviour:
																	_UpperCamelCase			     =     []
																	_UpperCamelCase			     =     [self.eos_token_id, self.cur_lang_code]
												else:
																	_UpperCamelCase			     =     [self.cur_lang_code]
																	_UpperCamelCase			     =     [self.eos_token_id]




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




